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Record W2094966695 · doi:10.1109/tuffc.2009.1070

Reflection from bound microbubbles at high ultrasound frequencies

2009· article· en· W2094966695 on OpenAlexaff
Olivier Couture, Michael Sprague, Emmanuel Chérin, Peter N. Burns, F. Stuart Foster

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2009
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsReflection (computer programming)MicrobubblesUltrasonic imagingUltrasoundAcousticsOpticsMaterials sciencePhysicsComputer science

Abstract

fetched live from OpenAlex

Targeted contrast agents and ultrasound imaging are now used in combination for the assessment and tracking of biomarkers in animal models in vivo. These applications have triggered interest in the understanding and prediction of the ultrasound echoes from contrast agents attached to cells. This study describes the reflection enhancement due to microbubbles bound on a gelatin surface. The reflection enhancement was measured using ultrasound pulses at high frequency (40 MHz) and low pressure (38 kPa peak-negativepressure) allowing a linear approximation to be applied. The observed reflection coefficient increased with the number of microbubbles, until reaching saturation at 0.9 when the surface coverage fraction was 35%. A multiple scattering model assuming that the targeted microbubbles are confined within an infinitesimally thin layer appeared suitable in predicting the reflection coefficient even at very high surface densities. These results could permit the optimization of the sensitivity of highfrequency ultrasound to targeted contrast agents.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.198
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2009
Admission routes1
Has abstractyes

Explore more

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